1. Field
The present invention relates generally to improving medical safety, efficiency and time management, reducing medical practice and system expenses, and more particularly, to a system and method for intelligent and enhanced laboratory data reporting, with diagnostic interpretations and recommendations as well as evidence-based recommendations for clinical actions. Among the many benefits provided by the inventions disclosed herein are improved medical diagnostic test utilization, service efficiency, increased meaningful use of EHR (electronic health records) technologies, and improved patient experience and satisfaction, all of which improve healthcare operations and patient outcomes in both outpatient and inpatient (hospital) practice settings.
2. Description of the Related Art
The state of the art in health care in the United States for medical analytic testing is marked by inefficiencies and archaic practices that have changed little over the last several decades, despite all the medical advances over that same period. FIG. 1 illustrates the prior art analytic cycle for medical testing, comprised of pre-analytic, analytic, and post-analytic phases. The first step in the pre-analytic phase is step 110, in which a physician or clinician orders a specific test or battery of tests for a patient. In step 120, the analytic test is performed, typically by collecting one or more specimens from the patent, e.g., a blood draw or urine specimen. The analytic phase consists of step 130, in which the analytic test is performed, typically by an analytic testing laboratory, as is known in the art. The post-analytic phase begins with step 140, in which the analytic test results are returned to the clinician who ordered the test. The testing center typically returns test results either in paper form or by faxing test results to the clinician's office. In step 150, the clinician reviews and analyzes the test results and any other documents regarding the analytic test performed. The patient is notified of the test results in step 160, usually by phone or mail. As part of step 160 the clinician may decide on a specific follow-up plan for the patient based on the test results, using the clinician's experience, knowledge and skill. Finally, in step 170 the patient is monitored through the follow-up plan devised by the clinician.
There are several problems in the prior art analytic cycle. First, the analytic cycle does not take advantage of the many advances made in computer processing and communications made in the last two decades. For example, analytic test results are primarily delivered in paper form or faxed in part due to HIPAA regulations regarding patient confidentiality and privacy. Second, the analytic cycle is time-consuming and inefficient. Third, the accuracy and efficacy of the diagnosis and interpretation of the analytic test results is largely a function of the clinician's knowledge and experience, and therefore varies widely among medical professionals.
The clinical laboratory is a major source of health care data. Increasingly these data are being integrated with other data to inform health system-wide actions meant to improve diagnostic test utilization, service efficiency, and increase “meaningful use.” Increasingly, much of the data created by a clinical laboratory is already coded and transmitted to electronic health records as discrete elements with meaningful flags, making it more amenable to analysis than text-based clinical histories and pathology reports. With rare exceptions, the current data reports still usually offer only three, simplistic conclusions, that being normal, high, or low, or “positive” and “negative” values. Occasionally, a reference to a journal article is included in the reporting for the clinician, for example when a lab report is returned for free and total PSA testing for prostate cancer screening. However, today there are very few systems in existence that truly provide an automated, intelligent report to providers and patients, with the breadth and scope of the inventions described herein. As health care systems are pressured to improve efficiency and reduce costs while improving patient satisfaction and clinical outcomes, such as mandates by the Centers for Medicare and Medicaid Services (CMS) “Triple Aim” goals, it will be increasingly important to leverage clinical laboratory data and advances that incorporate the expertise of the clinicians, pathologists and laboratorians that best understand diagnostic test data in the context of providing accurate, meaningful, and actionable reporting.
Global measures of diagnostic care quality are in their early infancy. National programs such as Physician Quality Reporting System (PQRS), Medicare Quality Payment Program (QPP), Merit-based Incentive Payment System (MIPS), and Health Plan Employer Data and Information Set (HEDIS), contain only a handful of diagnostic measures each, and so they cannot hope to assess in a balanced way the hundreds of thousands of diagnostic-related activities that occur today across the world of clinical medicine. Inventing new clinical quality measurement programs that are economically feasible yet have adequate breadth and balance represents an enormous challenge.
What is needed is a revolutionary new approach to medical diagnostic testing and interpretation that leverages the advances in artificial intelligence, machine learning, expert systems, and Internet-based communications to create a system and method for increasing efficiency of medical laboratory data interpretation, real time clinical decision support and provider and patient communications.